PS1: Pacman

CPSC 470/570 PS1: Pacman


Introduction
In this project, your Pacman agent will find paths through his maze world, both to reach a particular location
and to collect food efficiently. You will build general search algorithms and apply them to Pacman scenarios.
The code for this project consists of several Python files, some of which you will need to read and understand
in order to complete the assignment, and some of which you can ignore. You can download all the code and
supporting files from canvas.
Files you'll edit:
search.py: Where all of your search algorithms will reside.
searchAgents.py: Where all of your search-based agents will reside.
Files you might want to look at, but should not edit:
pacman.py: The main file that runs Pacman games. This file describes a Pacman GameState type, which
you use in this project.
game.py: The logic behind how the Pacman world works. This file describes several supporting types like
AgentState, Agent, Direction, and Grid.
util.py: Useful data structures for implementing search algorithms.
Supporting files you can ignore:
graphicsDisplay.py: Graphics for Pacman
graphicsUtils.py: Support for Pacman graphics
textDisplay.py: ASCII graphics for Pacman
ghostAgents.py: Agents to control ghosts
keyboardAgents.py: Keyboard interfaces to control Pacman
layout.py: Code for reading layout files and storing their contents
autograder.py: Project autograder
testParser.py: Parses autograder test and solution files
testClasses.py: General autograding test classes
test_cases/: Directory containing the test cases for each question
searchTestClasses.py: Project 1 specific autograding test classes
Files to Edit: You will fill in portions of search.py and searchAgents.py during the assignment. You may not
need to fill in all the "*** YOUR CODE HERE ***". Please do not change the other files in this distribution or
submit any of our original files other than these files.
Welcome to Pacman
After copying the code to your directory, you should be able to play a game of Pacman by typing the
following at the command line:
python3 pacman.py
Pacman lives in a shiny blue world of twisting corridors and tasty round treats. Navigating this world
efficiently will be Pacman's first step in mastering his domain.
The simplest agent in searchAgents.py is called the GoWestAgent, which always goes West (a trivial reflex
agent). This agent can occasionally win:
python3 pacman.py --layout testMaze --pacman GoWestAgent
But, things get ugly for this agent when turning is required:
python3 pacman.py --layout tinyMaze --pacman GoWestAgent
If Pacman gets stuck, you can exit the game by typing CTRL-c into your terminal.
Soon, your agent will solve not only tinyMaze, but any maze you want.
Note that pacman.py supports a number of options that can each be expressed in a long way (e.g., --layout)
or a short way (e.g., -l). You can see the list of all options and their default values via:
python3 pacman.py -h
Also, all of the commands that appear in this project also appear in commands.txt, for easy copying and
pasting. In UNIX/Mac OS X, you can even run all these commands in order with bash commands.txt.
Question 1 (3 points): Finding a Fixed Food Dot using Depth First
Search
In searchAgents.py, you'll find a fully implemented SearchAgent, which plans out a path through Pacman's
world and then executes that path step-by-step. The search algorithms for formulating a plan are not
implemented -- that's your job.
First, test that the SearchAgent is working correctly by running:
python3 pacman.py -l tinyMaze -p SearchAgent -a fn=tinyMazeSearch
The command above tells the SearchAgent to use tinyMazeSearch as its search algorithm, which is
implemented in search.py. Pacman should navigate the maze successfully.
Now it's time to write full-fledged generic search functions to help Pacman plan routes! Remember that a
search node must contain not only a state but also the information necessary to reconstruct the path (plan)
which gets to that state.
Important note: All of your search functions need to return a list of actions that will lead the agent from the
start to the goal. These actions all have to be legal moves (valid directions, no moving through walls).
Important note: Make sure to use the Stack, Queue and PriorityQueue data structures provided to you in
util.py! These data structure implementations have particular properties which are required for compatibility
with the autograder.
Hint: Each algorithm is very similar. Algorithms for DFS, BFS, UCS, and A* differ only in the details of how the
fringe is managed. So, concentrate on getting DFS right and the rest should be relatively straightforward.
Indeed, one possible implementation requires only a single generic search method which is configured with
an algorithm-specific queuing strategy. (Your implementation need not be of this form to receive full credit).
Implement the depth-first search (DFS) algorithm in the depthFirstSearch function in search.py. To make
your algorithm complete, write the graph search version of DFS, which avoids expanding any already visited
states.
Your code should quickly find a solution for:
python3 pacman.py -l tinyMaze -p SearchAgent
python3 pacman.py -l mediumMaze -p SearchAgent
python3 pacman.py -l bigMaze -z .5 -p SearchAgent
The Pacman board will show an overlay of the states explored, and the order in which they were explored
(brighter red means earlier exploration). Is the exploration order what you would have expected? Does
Pacman actually go to all the explored squares on his way to the goal?
Hint: If you use a Stack as your data structure, the solution found by your DFS algorithm for mediumMaze
should have a length of 130 (provided you push successors onto the fringe in the order provided by
getSuccessors; you might get 246 if you push them in the reverse order). Is this a least cost solution? If not,
think about what depth-first search is doing wrong. You don’t need to submit your answer to this question
though.
Question 2 (3 points): Breadth First Search
Implement the breadth-first search (BFS) algorithm in the breadthFirstSearch function in search.py. Again,
write a graph search algorithm that avoids expanding any already visited states. Test your code the same way
you did for depth-first search.
python3 pacman.py -l mediumMaze -p SearchAgent -a fn=bfs
python3 pacman.py -l bigMaze -p SearchAgent -a fn=bfs -z .5
Does BFS find a least cost solution? If not, check your implementation.
Hint: If Pacman moves too slowly for you, try the option --frameTime 0.
Note: If you've written your search code generically, your code should work equally well for the eight-puzzle
search problem without any changes.
python3 eightpuzzle.py
Question 3 (3 points): Varying the Cost Function
While BFS will find a fewest-actions path to the goal, we might want to find paths that are "best" in other
senses. Consider mediumDottedMaze and mediumScaryMaze.
By changing the cost function, we can encourage Pacman to find different paths. For example, we can charge
more for dangerous steps in ghost-ridden areas or less for steps in food-rich areas, and a rational Pacman
agent should adjust its behavior in response.
Implement the uniform-cost graph search algorithm in the uniformCostSearch function in search.py. We
encourage you to look through util.py for some data structures that may be useful in your implementation.
You should now observe successful behavior in all three of the following layouts, where the agents below are
all UCS agents that differ only in the cost function they use (the agents and cost functions are written for
you):
python3 pacman.py -l mediumMaze -p SearchAgent -a fn=ucs
python3 pacman.py -l mediumDottedMaze -p StayEastSearchAgent
python3 pacman.py -l mediumScaryMaze -p StayWestSearchAgent
Note: You should get very low and very high path costs for the StayEastSearchAgent and
StayWestSearchAgent respectively, due to their exponential cost functions (see searchAgents.py for details).
Question 4 (3 points): A* search
Implement A* graph search in the empty function aStarSearch in search.py. A* takes a heuristic function as
an argument. Heuristics take two arguments: a state in the search problem (the main argument), and the
problem itself (for reference information). The nullHeuristic heuristic function in search.py is a trivial
example.
You can test your A* implementation on the original problem of finding a path through a maze to a fixed
position using the Manhattan distance heuristic (implemented already as manhattanHeuristic in
searchAgents.py).
python3 pacman.py -l bigMaze -z .5 -p SearchAgent -a
fn=astar,heuristic=manhattanHeuristic
You should see that A* finds the optimal solution slightly faster than uniform cost search (about 549 vs. 620
search nodes expanded in our implementation, but ties in priority may make your numbers differ slightly).
Question 5 (4 points): Eating All The Dots
Now we'll solve a hard search problem: eating all the Pacman food in as few steps as possible. For this, we'll
need a new search problem definition which formalizes the food-clearing problem: FoodSearchProblem in
searchAgents.py (implemented for you). A solution is defined to be a path that collects all of the food in the
Pacman world. For the present project, solutions do not take into account any ghosts or power pellets;
solutions only depend on the placement of walls, regular food and Pacman. (Of course ghosts can ruin the
execution of a solution! We'll get to that in the next project.) If you have written your general search
methods correctly, A* with a null heuristic (equivalent to uniform-cost search) should quickly find an optimal
solution to testSearch with no code change on your part (total cost of 7).
python3 pacman.py -l testSearch -p AStarFoodSearchAgent
Note: AStarFoodSearchAgent is a shortcut for -p SearchAgent -a
fn=astar,prob=FoodSearchProblem,heuristic=foodHeuristic.
You should find that UCS starts to slow down even for the seemingly simple tinySearch. As a reference, our
implementation takes 2.5 seconds to find a path of length 27 after expanding 5057 search nodes.
Note: Make sure to complete Question 4 before working on Question 5, because Question 5 builds upon
your answer for Question 4.
Fill in foodHeuristic in searchAgents.py with a consistent heuristic for the FoodSearchProblem. Try your
agent on the trickySearch board:
python3 pacman.py -l trickySearch -p AStarFoodSearchAgent
Our UCS agent finds the optimal solution in about 13 seconds, exploring over 16,000 nodes. So don’t worry if
your code may take some time to run. But it should not take several minutes.
Any non-trivial non-negative consistent heuristic will receive 1 point. Make sure that your heuristic returns 0
at every goal state and never returns a negative value. Depending on how few nodes your heuristic expands,
you'll get additional points:
Number of nodes expanded Grade
More than 15000 1/4
At most 15000 2/4
At most 12000 3/4
At most 9000 4/4
At most 7000 5/4 (extra credit)
Remember: If your heuristic is inconsistent, you will receive no credit, so be careful! Can you solve
mediumSearch in a short time? If so, we're either very, very impressed, or your heuristic is inconsistent.
(For CPSC570 only) Question 6 (3 points): Suboptimal Search
This problem is required only for students enrolled in the graduate course (CPSC 570). Those in the
undergraduate course (CPSC 470) are welcome to try this out, but will not receive any credit for their work.
Sometimes, even with A* and a good heuristic, finding the optimal path through all the dots is hard. In these
cases, we'd still like to find a reasonably good path, quickly. In this section, you'll write an agent that always
greedily eats the closest dot. ClosestDotSearchAgent is implemented for you in searchAgents.py, but it's
missing a key function that finds a path to the closest dot.
Implement the function findPathToClosestDot in searchAgents.py. Our agent solves this maze
(suboptimally!) in under a second with a path cost of 350:
python3 pacman.py -l bigSearch -p ClosestDotSearchAgent -z .5
Hint: The quickest way to complete findPathToClosestDot is to fill in the AnyFoodSearchProblem, which is
missing its goal test. Then, solve that problem with an appropriate search function. The solution should be
very short!
Your ClosestDotSearchAgent won't always find the shortest possible path through the maze. Make sure you
understand why and try to come up with a small example where repeatedly going to the closest dot does not
result in finding the shortest path for eating all the dots. You don’t need to submit the answer though.
Submission
Please upload the following to canvas:
- search.py
- searchAgents.py